Open AccessAssorted Techniques for Defining Image Descriptors to Augment Content Based Classification Accuracy
Author(s) -
Rik Das,
Mohammad Arshad,
Pankaj Kumar Manjhi
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b4208.029320
Subject(s) - computer science , artificial intelligence , feature extraction , robustness (evolution) , pattern recognition (psychology) , domain (mathematical analysis) , feature (linguistics) , categorization , image processing , contextual image classification , image (mathematics) , feature detection (computer vision) , feature vector , content based image retrieval , digital image , computer vision , image retrieval , mathematics , linguistics , mathematical analysis , biochemistry , chemistry , philosophy , gene
Image data has turned out to be a significant means of expression with the advancements of digital image processing technologies. Image capturing devices has now transformed to commodities due to smart integration with cell phones and other useful devices. Huge amount of images are getting accumulated daily in gigantic databases which requires categorization for prompt retrieval in real time. Content based image classification (CBIC) thus gained it's popularity in classifying images to their corresponding categories. Feature extraction techniques are the foundation of CBIC to represent the image data in the form of feature vectors. This work has implemented three different feature extraction techniques from spatial domain, transform domain and deep learning domain. The three different feature vectors feature vector are contrasted to investigate the robustness of descriptor definition for content based image classification